Beginners Guide To Creating Artificial Neural Networks In R

The advancements in the field of artificial intelligence and machine learning are primarily focused on highly statistical programming languages. That’s why programming languages like R have been gaining immense popularity in the field, slowly but steadily closing in on the predominant Python. With libraries and packages available for all sorts of mathematical and statistical problems, R has already entered into the hearts and machines of many data scientists and machine learning enthusiasts.

In this tutorial, we will create a simple neural network using two hot libraries in R. Following this tutorial requires you to have:

Importing the library

Creating the neural network

Seeding is done to conserve the uniqueness in the predicted dataset. That is the predictions will always be the same for a specific seed. The code creates a neural network with N input nodes, two hidden layers with six nodes each and an output node.

Create a classifier for ANN

y: denotes the dependent factor. The value of y is the name of the column containing the dependent values.

training _frame: denotes the dataset the model uses to train. It should be an h2o object

activation: denotes the activation function applied to the nodes

train_samples_per_epoch: denotes the batch_size the model uses to train with

epochs: one epoch stands for one complete training of the neural network with all samples.

The number of nodes are random and there in no fixed optimal values. We do not have to mention the number of nodes in the input as h2o directly identifies everything except ‘y’ in the training set as independent factors.

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